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Transcriptomics in T2-high severe asthmatics treated with biologics: Comparison between anti-IgE, anti-IL-5 and anti-IL-5R-targeted treatments.

GSE298740 Homo sapiens (human) RNA-seq (gene expression) 70 samples Submitted 2025/12/13 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 70 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
70 samples: 70 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Transcriptomics in T2-High Severe Asthmatics Treated with Biologics: Comparison between Anti-IgE, Anti-IL-5, and Anti-IL-5 Receptor TreatmentsAmerican journal of respiratory cell and molecular biology · 2025
Summary (written by the submitters)
Background : Biologic therapies have transformed severe asthma management, particularly for T2-high asthma. We aimed to investigate blood proteomics and transcriptomics after biologic treatment. Methods : A prospective study was conducted on 35 patients with severe T2-high asthma treated with mepolizumab (n=9), benralizumab (n=14), or omalizumab (n=12).
Read the full summary ↓
Evaluations occurred at baseline and after 16 weeks (omalizumab) or 24 weeks (mepolizumab, benralizumab) of treatment. Blood samples were collected. Gene expression was analyzed via Illumina TruSeq RNA Prep, with RNA sequencing on the Illumina NovaSeq-6000 platform. Results : RNA-seq analysis revealed 86 down-regulated genes common to both mepolizumab and benralizumab, primarily associated with eosinophils, basophils, and the purinergic receptor signaling pathway. Additionally, there was evidence of gene regulation involved in antiviral immunity. However, specific gene modulation was observed for benralizumab (114 genes) and mepolizumab (68 genes). Changes following omalizumab treatment were minimal, with only three significantly down-regulated genes. Conclusion : While omalizumab showed minimal quantifiable effects on gene expression, mepolizumab and benralizumab—despite exhibiting distinct profiles—both induced down-regulation of genes related to eosinophil and basophil biology and purinergic signaling pathways, alongside up-regulation of genes involved in innate antiviral immunity.
Published in
Transcriptomics in T2-High Severe Asthmatics Treated with Biologics: Comparison between Anti-IgE, Anti-IL-5, and Anti-IL-5 Receptor Treatments
Gerday S, Schleich F, Sabbe M et al. · American journal of respiratory cell and molecular biology 2025 · PMID 40700724 · doi:10.1165/rcmb.2025-0188LE
This dataset
Open GSE298740 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE298740_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1271043 and SRA study SRP589424. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40700724) ↗
Study design
70 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 70 sample titles: 70 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 30 more — browse all 70 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.